首页|University of Adelaide Reports Findings in Cancer Biomarkers (Graphene and metal -organic framework hybrids for highperformance sensors for lung cancer biomarker detection supported by machine learning augmentation)
University of Adelaide Reports Findings in Cancer Biomarkers (Graphene and metal -organic framework hybrids for highperformance sensors for lung cancer biomarker detection supported by machine learning augmentation)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News - New research on Diagnostics and Screen ing - Cancer Biomarkers is the subject ofa report. According to news reporting originating in Adelaide, Australia, by NewsRx journalists, researchstated, “Con ventional diagnostic methods for lung cancer, based on breath analysis using gas chromatographyand mass spectrometry, have limitations for fast screening due t o their limited availability, operationalcomplexity, and high cost. As potentia l replacement, among several low-cost and portable methods,chemoresistive senso rs for the detection of volatile organic compounds (VOCs) that represent biomark ersof lung cancer were explored as promising solutions, which unfortunately sti ll face challenges.”Financial support for this research came from Australian Research Council.The news reporters obtained a quote from the research from the University of Ade laide, “To addressthe key problems of these sensors, such as low sensitivity, h igh response time, and poor selectivity, thisstudy presents the design of new c hemoresistive sensors based on hybridised porous zeolitic imidazolate(ZIF-8) ba sed metal-organic frameworks (MOFs) and laser-scribed graphene (LSG) structures, inspiredby the architecture of the human lung. The sensing performance of the fabricated ZIF-8@LSG hybridsensors was characterised using four dominant VOC bi omarkers, including acetone, ethanol, methanol,and formaldehyde, which are iden tified as metabolomic signatures in lung cancer patients’ exhaled breath.The re sults using simulated breath samples showed that the sensors exhibited excellent performance for aset of these biomarkers, including fast response (2-3 seconds ), a wide detection range (0.8 ppm to 50 ppm),a low detection limit (0.8 ppm), and high selectivity, all obtained at room temperature. Intelligent machinelear ning (ML) recognition using the multilayer perceptron (MLP)-based classification algorithm was furtheremployed to enhance the capability of these sensors, achi eving an exceptional accuracy (approximately96.5%) for the four ta rgeted VOCs over the tested range (0.8-10 ppm).”
AdelaideAustraliaAustralia and New ZealandBiomarkersCancerCancer BiomarkersCarcinoma BiomarkersCyborgsDiagnostics and ScreeningEmerging TechnologiesHealth and MedicineLung CancerLung Diseases and ConditionsLung NeoplasmsMachine LearningOncology
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